Predicting final disposition after stroke using the Orpington Prognostic Score
Bibliographic record
Abstract
Background: Prediction of outcome after stroke is important for triage decisions, prognostic estimates for family and for appropriate resource utilization. Prognostication must be timely and simply applied. Several scales have shown good prognostic value. In Calgary, the Orpington Prognostic Score (OPS) has been used to predict outcome as an aid to rehabilitation triage. However, the OPS has not been assessed at one week for predictive capability. Methods: Among patients admitted to a sub-acute stroke unit, OPS from the first week were examined to determine if any correlation existed between final disposition after rehabilitation and first week score. The predictive validity of the OPS at one week was compared to National Institute of Health Stroke Scale (NIHSS) score at 24 hours using logistic regression and receiver operator characteristics analysis. The primary outcome was final disposition after discharge from the stroke unit if the patient went directly home, or died, or from the inpatient rehabilitation unit. Results: The first week OPS was highly predictive of final disposition. However, no major advantage in using the first week OPS was observed when compared to 24h NIHSS score. Both scales were equally predictive of final disposition of stroke patients, post rehabilitation. Conclusion: The first week OPS can be used to predict final outcome. The NIHSS at 24h provides the same prognostic information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".